List of updateMarkov() Examples

Examples of updateMarkov()

statechum.analysis.learning.MarkovClassifier.updateMarkov()
Updates Markov. This is useful where we have added something to the original PTA and need to update Markov. Crucial for learning of Markov for sideways inference, where we cannot learn from the original traces and have to delay Markov construction to the time where PTA is built.
Note that computing Markov using incoming/outgoing paths of length 0 is just a distribution of letters, tagged with pos/neg/fail. It is not based on earlier knowledge hence could be built either forwards or sideways in the same way. @param vert state to predict for @param alphabet alphabet of the graph of interest @param chunkLength how many steps to make a prediction for.
statechum.analysis.learning.MarkovUniversalLearner.updateMarkov()
Updates Markov based on predictions. This is useful where we have added something to the original PTA and need to update Markov. Crucial for learning of Markov for sideways inference, where we cannot learn from the original traces and have to delay Markov construction to the time where PTA is built.
Note that computing Markov using incoming/outgoing paths of length 0 is just a distribution of letters, tagged with pos/neg/fail. It is not based on earlier knowledge hence could be built either forwards or sideways in the same way. @param graph the graph to predict transitions for @param markovMatrix the matrix used for predictions @param Inverse_Graph graph used to make predictions @param predictForwardOrSideways whether to predict forward (true) or sideways (false). @param vert state to predict for @param alphabet alphabet of the graph of interest @param chunkLength how many steps to make a prediction for.

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

    /*
    m.updateMarkov(merged,predictForwardOrSideways,false);// now we construct sideways learner ...
    m.constructMarkovTentative(graph,predictForwardOrSideways);// ... and use it to add more transitions.
    */
    MarkovModel inverseModel = new MarkovModel(ptaClassifier.model.getChunkLen(),true,!ptaClassifier.model.directionForwardOrInverse);
    MarkovClassifier cl = new MarkovClassifier(inverseModel,ptaClassifier.graph);cl.updateMarkov(false);
    Collection<Set<CmpVertex>> verticesToMergeUsingSideways=cl.buildVerticesToMergeForPaths(pathsOfInterest);
    return verticesToMergeUsingSideways;
  }
  
  public static LearnerGraph checkIfSingleStateLoopsCanBeFormed(MarkovClassifier ptaClassifier,LearnerGraph referenceGraph,final Collection<List<Label>> pathsOfInterest)

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

  @Test
  public void testMarkovPerformance1()
  {
    final LearnerGraph trainingGraph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    statechum.Pair<Double,Double> pairTraining = cl.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(2./3,pairTraining.firstElem,Configuration.fpAccuracy);// reflects that transitions u and c from G are not present but predicted
    Assert.assertEquals(2./3.,pairTraining.secondElem,Configuration.fpAccuracy);// reflects that transitions a and c are not predicted but present.
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-t->B","testMarkovPerformance1a",config, converter));

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

  @Test
  public void testMarkovPerformance2()
  {
    final LearnerGraph trainingGraph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-u-#D / B-b->G","testMarkovPerformance2",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(1,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(2./3,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted
  }

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

  @Test
  public void testMarkovPerformance3()
  {
    final LearnerGraph trainingGraph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-u-#D / B-b->G / B-e->Z","testMarkovPerformance3",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(1,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0.5,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted
  }

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

  @Test
  public void testMarkovPerformance4()
  {
    final LearnerGraph trainingGraph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-b->C-c->D-u->E","testMarkovPerformance4",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(3./4,pair.firstElem,Configuration.fpAccuracy);// u is predicted as negative and is indeed missing, b is correctly predicted as a positive; u after c is correctly predicted as positive and c after c is not correctly predicted.
    Assert.assertEquals(0.5,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

  @Test
  public void testMarkovPerformance5()
  {
    final LearnerGraph trainingGraph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-b->G","testMarkovPerformance5",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(1,pair.firstElem,Configuration.fpAccuracy);// u is predicted as negative and is indeed missing, b is correctly predicted as a positive
    Assert.assertEquals(0.5,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

    /*
    m.updateMarkov(merged,predictForwardOrSideways,false);// now we construct sideways learner ...
    m.constructMarkovTentative(graph,predictForwardOrSideways);// ... and use it to add more transitions.
    */
    MarkovModel inverseModel = new MarkovModel(ptaClassifier.model.getChunkLen(),true,!ptaClassifier.model.directionForwardOrInverse);
    MarkovClassifier cl = new MarkovClassifier(inverseModel,ptaClassifier.graph);cl.updateMarkov(false);
    Collection<Set<CmpVertex>> verticesToMergeUsingSideways=cl.buildVerticesToMergeForPaths(pathsOfInterest);
    return verticesToMergeUsingSideways;
  }
  
  public static LearnerGraph checkIfSingleStateLoopsCanBeFormed(MarkovClassifier ptaClassifier,LearnerGraph referenceGraph,final Collection<List<Label>> pathsOfInterest)

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

  @Test
  public void testMarkovPerformance1()
  {
    final LearnerGraph trainingGraph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    statechum.Pair<Double,Double> pairTraining = cl.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(2./3,pairTraining.firstElem,Configuration.fpAccuracy);// reflects that transitions u and c from G are not present but predicted
    Assert.assertEquals(2./3.,pairTraining.secondElem,Configuration.fpAccuracy);// reflects that transitions a and c are not predicted but present.
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-t->B","testMarkovPerformance1a",config, converter));

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

  @Test
  public void testMarkovPerformance2()
  {
    final LearnerGraph trainingGraph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-u-#D / B-b->G","testMarkovPerformance2",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(1,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(2./3,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted
  }

View Full Code Here

Examples of statechum.analysis.learning.MarkovClassifier.updateMarkov()

  @Test
  public void testMarkovPerformance3()
  {
    final LearnerGraph trainingGraph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-u-#D / B-b->G / B-e->Z","testMarkovPerformance3",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(1,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0.5,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted
  }

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.